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An AI-built app working once proves only that one path succeeded in one environment. It does not prove that dependencies, runtime settings, deployment configuration, external services, or later edits will behave the same way. When it breaks, capture the exact failure, find which boundary it occurs at, inspect the relevant logs and environment, and change one likely cause at a time.
Why an app can work once and then fail
A successful preview or local run is a narrow demonstration, not proof that the app can be reproduced elsewhere. A project may rely on a package or runtime detail that is missing from its declared dependencies, or on settings that differ between local, preview, and production environments. A 2026 empirical study examined dependency gaps in projects generated by coding agents, illustrating why a working result may be difficult to recreate reliably: the study of dependency gaps in LLM-based coding agents.
Other failures happen beyond the app itself. A request may fail at the browser, application server, database or other dependency, deployment startup, network, or external API. AI applications can also misbehave in less obvious ways than a page that simply refuses to load. Microsoft’s production guidance recommends checking logs and whether dependencies are reachable when model calls fail or time out: Production AI practices.
Debug the failure in a repeatable sequence
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Capture the symptom before changing anything
Write down the screen or route, the actions and input that led to the problem, the timestamp, and whether it happened locally, in preview, or in production. Save the first error message and note whether the failure is consistent or intermittent. This gives you a specific path to repeat rather than a vague report that the app is broken.
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Locate where the failure occurs
Decide whether the symptom points to the browser or UI, application server, database or another dependency, deployment startup, or an external API. Check the relevant application and deployment logs. If the app calls an AI provider, compare the failure time with provider-side request data to see whether the request arrived.
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Compare configuration and dependencies
Compare the local and deployed environments, including required services, credentials, permissions, and installed dependencies. Check whether the project can be set up again from its declared dependency state; the set of packages declared by a project may not capture every dependency it needs at runtime. Microsoft’s agent troubleshooting guidance also recommends checking prerequisites such as dependencies, services, authentication, and permissions: Get an agent back on track.
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Scope API evidence to the affected request
When an API call is involved, use the provider’s request and error views to examine the relevant project and model, HTTP status codes, error rates, and timing. OpenAI’s guidance describes using scoped API health data to investigate errors and latency: Troubleshooting API Errors and Latency. If the provider shows no matching event, investigate client-side timeouts, proxies, network conditions, or load balancers; the request may have failed before reaching the provider.
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Ask the coding agent to diagnose before editing
Give the agent the reproduction steps, exact command or tool that failed, first error, relevant logs, and the smallest affected area. Ask it to identify the evidence for a proposed cause before making a narrow change. A specific failure report is more actionable than asking it to fix the whole app; Microsoft’s guidance likewise emphasizes the failed command, first error, diagnostic evidence, and prerequisites.
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Repeat the failing path and preserve the working version
After a change, run the original sequence again and check the adjacent flow most likely to share the affected code. Keep a known-good version available so you can identify or undo a regression. Version control and reproducible, auditable artifacts are part of AWS guidance for hardening generative-AI applications: Hardening the generative AI application through a GenAIOps framework.
What different failure clues can suggest
- Works locally but not after deployment: compare runtime settings, dependency installation, required services, authentication, and permissions.
- Fails only when calling an API: inspect status codes, provider-side request data, timeouts, and network or proxy conditions to distinguish service errors from failures on the client’s path.
- Fails intermittently: investigate transient conditions such as rate limits, network problems, timeouts, or server errors rather than assuming the app logic is always at fault.
- Appears to work but behaves incorrectly: inspect logs and dependency reachability; an AI feature can fail subtly without producing a simple page-load error.
- Breaks after a code or setup change: compare with the last known-good version and make the change history reproducible and auditable.
These are diagnostic possibilities, not a measured ranking of causes across all AI-built apps. The right clue depends on the failure and where it occurs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When retries help—and when they do not
Retries can help with transient failures, but they do not repair persistent logic errors or incorrect configuration. Google says its official Python Gemini SDKs use exponential backoff by default for transient timeouts, network issues, rate limits, and 5xx errors. Retry behavior varies by SDK and configuration, so check the SDK your app actually uses: Gemini API troubleshooting.
If the same failure persists, repeating the request may only repeat the symptom. Use the error and logs to determine whether the cause is transient before treating retries as a solution.
Best Value
What the 2026 dependency study does—and does not—show
The study of AI-generated code reports that its authors evaluated three coding agents across 300 projects using 100 standardized prompts. For projects that eventually ran, the authors report an average of 15 minutes of manual debugging. Those figures describe that experiment only; they are not a general failure rate, a typical repair time for an individual app, or a forecast of how long your own debugging will take. The paper is available at AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents.
Quick Recap
Make the next failure easier to diagnose
- Keep the reproduction steps, first error, and relevant logs together.
- Track code and configuration changes so you can compare against a known-good version.
- Make setup and dependencies reproducible rather than relying on an undocumented working environment.
- After a fix, rerun the original user path and a nearby flow that may share the affected code.
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